ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Marine Affairs and Policy
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1551352
This article is part of the Research TopicChallenges and Solutions in Forecasting and Decision-Making in Marine Economy and Management, Volume IIView all 5 articles
Prediction and analysis of China's coastal marine economy: An innovative grey model with the best-matching data-preprocessing techniques
Provisionally accepted
- 1Anhui Science and Technology University, Bengbu, China
- 2Zhejiang University of Finance and Economics, Hangzhou, Zhejiang Province, China
- 3Ningbo University of Finance and Economics, Ningbo, China
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China's coastal marine economy, a key part of the national economy, exhibits complex temporal evolution and regional heterogeneity, posing challenges for accurate forecasting.To address these challenges, this study employs advanced data-preprocessing techniques, accumulating generation operators (AGO) in grey prediction models, to tackle the nonlinear, volatile, and heterogeneous gross ocean product (GOP) data. Specifically, an accumulating generation operator matching mechanism that utilizes a pool of seven advanced AGOs is incorporated into the discrete grey prediction model. The proposed best-matching discrete grey prediction model can accurately describe the GOP system in China's 11 coastal provinces.Furthermore, the experimental results indicate that the proposed model achieves 5.09% average forecasting mean absolute percentage error, demonstrating 46.65% and 61.73% improvement rates over the single AGO-based and benchmark models, respectively. Consequently, the proposed model is deployed to forecast China's provincial GOP up to 2025, offering insights into the national development strategies, regionally tailored policies, and inter-provincial coordination in the marine sector.
Keywords: Marine economy forecasting, Gross ocean product, Grey prediction model, Accumulating generation operator, Data Preprocessing
Received: 25 Dec 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Xi, Cai, Li and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zerong Wang, Ningbo University of Finance and Economics, Ningbo, China
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